Hadoop生态大数据系统分为Yam、 HDFS、MapReduce计算框架。TensorFlow分布式相当于MapReduce计算框架，Kubernetes相当于Yam调度系统。TensorFlowOnSpark，利用远程直接内存访问(Remote Direct Memory Access,RDMA)解决存储功能和调度，实现深度学习和大数据融合。TensorFlowOnSpark(TFoS)，雅虎开源项目。 。支持ApacheSpark集群分布式TensorFlow训练、预测。TensorFlowOnSpark提供桥接程序，每个Spark Executor启动一个对应TensorFlow进程，通过远程进程通信(RPC)交互。

TensorFlowOnSpark架构。TensorFlow训练程序用Spark集群运行，管理Spark集群步骤：预留，在Executor执行每个TensorFlow进程保留一个端口，启动数据消息监听器。启动，在Executor启动TensorFlow主函数。数据获取，TensorFlow Readers和QueueRunners机制直接读取HDFS数据文件，Spark不访问数据；Feeding，SparkRDD 数据发送TensorFlow节点，数据通过feed_dict机制传入TensorFlow计算图。关闭，关闭Executor TensorFlow计算节点、参数服务节点。Spark Driver->Spark Executor->参数服务器->TensorFlow Core->gRPC、RDMA->HDFS数据集。 。

TensorFlowOnSpark MNIST。 。Standalone模式Spark集群，一台计算机。安装 Spark、Hadoop。部署Java 1.8.0 JDK。下载Spark2.1.0版 。下载Hadoop2.7.3版 。0.12.1版本支持较好。

修改配置文件，设置环境变量，启动Hadoop:$HADOOP_HOME/sbin/start-all.sh。检出TensorFlowOnSpark源代码：

git clone --recurse-submodules https://github.com/yahoo/TensorFlowOnSpark.gitcd TensorFlowOnSparkgit submodule initgit submodule update --forcegit submodule foreach --recursive git clean -dfx

源代码打包，提交任务使用：

cd TensorflowOnSpark/srczip -r ../tfspark.zip *

设置TensorFlowOnSpark根目录环境变量：

cd TensorFlowOnSparkexport TFoS_HOME=$(pwd)

启动Spark主节点(master):

$(SPARK_HOME)/sbin/start-master.sh

配置两个工作节点(worker)实例，master-spark-URL连接主节点：

export MASTER=spark://$(hostname):7077export SPARK_WORKER_INSTANCES=2export CORES_PER_WORKER=1export TOTAL_CORES=$(($(CORES_PER_WORKER)*$(SPARK_WORKER_INSTANCES)))$(SPARK_HOME)/sbin/start-slave.sh -c $CORES_PER_WORKER -m 3G $(MASTER)

提交任务，MNIST zip文件转换为HDFS RDD 数据集：

$(SPARK_HOME)/bin/spark-submit \--master $(MASTER) --conf spark.ui.port=4048 --verbose \$(TFoS_HOME)/examples/mnist/mnist_data_setup.py \--output examples/mnist/csv \--format csv

查看处理过的数据集：

hadoop fs -ls hdfs://localhost:9000/user/libinggen/examples/mnist/csv

查看保存图片、标记向量：

hadoop fs -ls hdfs://localhost:9000/user/libinggen/examples/mnist/csv/train/labels

把训练集、测试集分别保存RDD数据。

。

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport numpyimport tensorflow as tffrom array import arrayfrom tensorflow.contrib.learn.python.learn.datasets import mnistdef toTFExample(image, label):  """Serializes an image/label as a TFExample byte string"""  example = tf.train.Example(    features = tf.train.Features(      feature = {        'label': tf.train.Feature(int64_list=tf.train.Int64List(value=label.astype("int64"))),        'image': tf.train.Feature(int64_list=tf.train.Int64List(value=image.astype("int64")))      }    )  )  return example.SerializeToString()def fromTFExample(bytestr):  """Deserializes a TFExample from a byte string"""  example = tf.train.Example()  example.ParseFromString(bytestr)  return exampledef toCSV(vec):  """Converts a vector/array into a CSV string"""  return ','.join([str(i) for i in vec])def fromCSV(s):  """Converts a CSV string to a vector/array"""  return [float(x) for x in s.split(',') if len(s) > 0]def writeMNIST(sc, input_images, input_labels, output, format, num_partitions):  """Writes MNIST image/label vectors into parallelized files on HDFS"""  # load MNIST gzip into memory  # MNIST图像、标记向量写入HDFS  with open(input_images, 'rb') as f:    images = numpy.array(mnist.extract_images(f))  with open(input_labels, 'rb') as f:    if format == "csv2":      labels = numpy.array(mnist.extract_labels(f, one_hot=False))    else:      labels = numpy.array(mnist.extract_labels(f, one_hot=True))  shape = images.shape  print("images.shape: {0}".format(shape))          # 60000 x 28 x 28  print("labels.shape: {0}".format(labels.shape))   # 60000 x 10  # create RDDs of vectors  imageRDD = sc.parallelize(images.reshape(shape[0], shape[1] * shape[2]), num_partitions)  labelRDD = sc.parallelize(labels, num_partitions)  output_images = output + "/images"  output_labels = output + "/labels"  # save RDDs as specific format  # RDDs保存特定格式  if format == "pickle":    imageRDD.saveAsPickleFile(output_images)    labelRDD.saveAsPickleFile(output_labels)  elif format == "csv":    imageRDD.map(toCSV).saveAsTextFile(output_images)    labelRDD.map(toCSV).saveAsTextFile(output_labels)  elif format == "csv2":    imageRDD.map(toCSV).zip(labelRDD).map(lambda x: str(x[1]) + "|" + x[0]).saveAsTextFile(output)  else: # format == "tfr":    tfRDD = imageRDD.zip(labelRDD).map(lambda x: (bytearray(toTFExample(x[0], x[1])), None))    # requires: --jars tensorflow-hadoop-1.0-SNAPSHOT.jar    tfRDD.saveAsNewAPIHadoopFile(output, "org.tensorflow.hadoop.io.TFRecordFileOutputFormat",                                keyClass="org.apache.hadoop.io.BytesWritable",                                valueClass="org.apache.hadoop.io.NullWritable")#  Note: this creates TFRecord files w/o requiring a custom Input/Output format#  else: # format == "tfr":#    def writeTFRecords(index, iter):#      output_path = "{0}/part-{1:05d}".format(output, index)#      writer = tf.python_io.TFRecordWriter(output_path)#      for example in iter:#        writer.write(example)#      return [output_path]#    tfRDD = imageRDD.zip(labelRDD).map(lambda x: toTFExample(x[0], x[1]))#    tfRDD.mapPartitionsWithIndex(writeTFRecords).collect()def readMNIST(sc, output, format):  """Reads/verifies previously created output"""  output_images = output + "/images"  output_labels = output + "/labels"  imageRDD = None  labelRDD = None  if format == "pickle":    imageRDD = sc.pickleFile(output_images)    labelRDD = sc.pickleFile(output_labels)  elif format == "csv":    imageRDD = sc.textFile(output_images).map(fromCSV)    labelRDD = sc.textFile(output_labels).map(fromCSV)  else: # format.startswith("tf"):    # requires: --jars tensorflow-hadoop-1.0-SNAPSHOT.jar    tfRDD = sc.newAPIHadoopFile(output, "org.tensorflow.hadoop.io.TFRecordFileInputFormat",                              keyClass="org.apache.hadoop.io.BytesWritable",                              valueClass="org.apache.hadoop.io.NullWritable")    imageRDD = tfRDD.map(lambda x: fromTFExample(str(x[0])))  num_images = imageRDD.count()  num_labels = labelRDD.count() if labelRDD is not None else num_images  samples = imageRDD.take(10)  print("num_images: ", num_images)  print("num_labels: ", num_labels)  print("samples: ", samples)if __name__ == "__main__":  import argparse  from pyspark.context import SparkContext  from pyspark.conf import SparkConf  parser = argparse.ArgumentParser()  parser.add_argument("-f", "--format", help="output format", choices=["csv","csv2","pickle","tf","tfr"], default="csv")  parser.add_argument("-n", "--num-partitions", help="Number of output partitions", type=int, default=10)  parser.add_argument("-o", "--output", help="HDFS directory to save examples in parallelized format", default="mnist_data")  parser.add_argument("-r", "--read", help="read previously saved examples", action="store_true")  parser.add_argument("-v", "--verify", help="verify saved examples after writing", action="store_true")

args = parser.parse_args()

print("args:",args)  sc = SparkContext(conf=SparkConf().setAppName("mnist_parallelize"))  if not args.read:    # Note: these files are inside the mnist.zip file    writeMNIST(sc, "mnist/train-images-idx3-ubyte.gz", "mnist/train-labels-idx1-ubyte.gz", args.output + "/train", args.format, args.num_partitions)    writeMNIST(sc, "mnist/t10k-images-idx3-ubyte.gz", "mnist/t10k-labels-idx1-ubyte.gz", args.output + "/test", args.format, args.num_partitions)  if args.read or args.verify:    readMNIST(sc, args.output + "/train", args.format)

提交训练任务，开始训练，在HDFS生成mnist_model，命令：

${SPARK_HOME}/bin/spark-submit \--master ${MASTER} \--py-files ${TFoS_HOME}/examples/mnist/spark/mnist_dist.py \--conf spark.cores.max=${TOTAL_CORES} \--conf spark.task.cpus=${CORES_PER_WORKER} \--conf spark.executorEnv.JAVA_HOME="$JAVA_HOME" \${TFoS_HOME}/examples/mnist/spark/mnist_spark.py \--cluster_size ${SPARK_WORKER_INSTANCES} \--images examples/mnist/csv/train/images \--labels examples/mnist/csv/train/labels \--format csv \--mode train \--model mnist_model

mnist_dist.py 构建TensorFlow 分布式任务，定义分布式任务主函数，启动TensorFlow主函数map_fun，数据获取方式Feeding。获取TensorFlow集群和服务器实例：

cluster, server = TFNode.start_cluster_server(ctx, 1, args.rdma)

TFNode调用tfspark.zip TFNode.py文件。

mnist_spark.py文件是训练主程序，TensorFlowOnSpark部署步骤：

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionfrom pyspark.context import SparkContextfrom pyspark.conf import SparkConfimport argparseimport osimport numpyimport sysimport tensorflow as tfimport threadingimport timefrom datetime import datetimefrom tensorflowonspark import TFClusterimport mnist_distsc = SparkContext(conf=SparkConf().setAppName("mnist_spark"))executors = sc._conf.get("spark.executor.instances")num_executors = int(executors) if executors is not None else 1num_ps = 1parser = argparse.ArgumentParser()parser.add_argument("-b", "--batch_size", help="number of records per batch", type=int, default=100)parser.add_argument("-e", "--epochs", help="number of epochs", type=int, default=1)parser.add_argument("-f", "--format", help="example format: (csv|pickle|tfr)", choices=["csv","pickle","tfr"], default="csv")parser.add_argument("-i", "--images", help="HDFS path to MNIST images in parallelized format")parser.add_argument("-l", "--labels", help="HDFS path to MNIST labels in parallelized format")parser.add_argument("-m", "--model", help="HDFS path to save/load model during train/inference", default="mnist_model")parser.add_argument("-n", "--cluster_size", help="number of nodes in the cluster", type=int, default=num_executors)parser.add_argument("-o", "--output", help="HDFS path to save test/inference output", default="predictions")parser.add_argument("-r", "--readers", help="number of reader/enqueue threads", type=int, default=1)parser.add_argument("-s", "--steps", help="maximum number of steps", type=int, default=1000)parser.add_argument("-tb", "--tensorboard", help="launch tensorboard process", action="store_true")parser.add_argument("-X", "--mode", help="train|inference", default="train")parser.add_argument("-c", "--rdma", help="use rdma connection", default=False)args = parser.parse_args()print("args:",args)print("{0} ===== Start".format(datetime.now().isoformat()))if args.format == "tfr":  images = sc.newAPIHadoopFile(args.images, "org.tensorflow.hadoop.io.TFRecordFileInputFormat",                              keyClass="org.apache.hadoop.io.BytesWritable",                              valueClass="org.apache.hadoop.io.NullWritable")  def toNumpy(bytestr):    example = tf.train.Example()    example.ParseFromString(bytestr)    features = example.features.feature    image = numpy.array(features['image'].int64_list.value)    label = numpy.array(features['label'].int64_list.value)    return (image, label)  dataRDD = images.map(lambda x: toNumpy(str(x[0])))else:  if args.format == "csv":    images = sc.textFile(args.images).map(lambda ln: [int(x) for x in ln.split(',')])    labels = sc.textFile(args.labels).map(lambda ln: [float(x) for x in ln.split(',')])  else: # args.format == "pickle":    images = sc.pickleFile(args.images)    labels = sc.pickleFile(args.labels)  print("zipping images and labels")  dataRDD = images.zip(labels)#1.为在Executor执行每个TensorFlow进程保留一个端口cluster = TFCluster.run(sc, mnist_dist.map_fun, args, args.cluster_size, num_ps, args.tensorboard, TFCluster.InputMode.SPARK)#2.启动Tensorflow主函数cluster.start(mnist_dist.map_fun, args)if args.mode == "train":  #3.训练  cluster.train(dataRDD, args.epochs)else:  #3.预测  labelRDD = cluster.inference(dataRDD)  labelRDD.saveAsTextFile(args.output)#4.关闭Executor TensorFlow计算节点、参数服务节点cluster.shutdown()print("{0} ===== Stop".format(datetime.now().isoformat()))

预测命令：

${SPARK_HOME}/bin/spark-submit \--master ${MASTER} \--py-files ${TFoS_HOME}/examples/mnist/spark/mnist_dist.py \--conf spark.cores.max=${TOTAL_CORES} \--conf spark.task.cpus=${CORES_PER_WORKER} \--conf spark.executorEnv.JAVA_HOME="$JAVA_HOME" \${TFoS_HOME}/examples/mnist/spark/mnist_spark.py \--cluster_size ${SPARK_WORKER_INSTANCES} \--images examples/mnist/csv/test/images \--labels examples/mnist/csv/test/labels \--mode inference \--format csv \--model mnist_model \--output predictions

还可以Amazon EC2运行及在Hadoop集群采用YARN模式运行。

参考资料：

《TensorFlow技术解析与实战》

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